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Temporal correlations among demographic parameters are ubiquitous but highly variable across species.

Rémi FaySandra HamelMartijn van de PolJean-Michel GaillardNigel Gilles YoccozPaul AckerMatthieu AuthierBenjamin LarueChristie Le CoeurKaitlin R MacdonaldAlex Nicol-HarperChristophe BarbraudChristophe BonenfantDirk H Van VurenEmmanuelle CamKarine DelordMarlène GamelonMaria MoironFanie PelletierJay J RotellaCéline TeplitskyMarcel E VisserCaitlin P WellsNathaniel T WheelwrightStéphanie JenouvrierBernt-Erik Saether
Published in: Ecology letters (2022)
Temporal correlations among demographic parameters can strongly influence population dynamics. Our empirical knowledge, however, is very limited regarding the direction and the magnitude of these correlations and how they vary among demographic parameters and species' life histories. Here, we use long-term demographic data from 15 bird and mammal species with contrasting pace of life to quantify correlation patterns among five key demographic parameters: juvenile and adult survival, reproductive probability, reproductive success and productivity. Correlations among demographic parameters were ubiquitous, more frequently positive than negative, but strongly differed across species. Correlations did not markedly change along the slow-fast continuum of life histories, suggesting that they were more strongly driven by ecological than evolutionary factors. As positive temporal demographic correlations decrease the mean of the long-run population growth rate, the common practice of ignoring temporal correlations in population models could lead to the underestimation of extinction risks in most species.
Keyphrases
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  • climate change
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  • machine learning
  • deep learning